A visual review of artificial intelligence and Industry 4.0 in healthcare

Sood, Sandeep K.; Rawat, Keshav Singh; Kumar, Dheeraj

doi:10.1016/j.compeleceng.2022.107948

Cited by 36 publications

(6 citation statements)

References 25 publications

(25 reference statements)

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“…The findings presented in Figure 1 indicate that the agri-food industry is a widely discussed topics among scholars, constituting 30% of the total frequency, followed by manufacturing and healthcare supply chain, accounting for 27% and 15% of selected papers respectively. The obtained result is consistent with the current research and industry development, as the increasing use of AI in the agricultural and food industry is driven by the need for food security (Bhatia and Albarrak, 2023), traceability (Hassoun et al, 2023) and sustainability (Sharma et al, 2022); the use of AI in manufacturing supply chain motivated by circular economy (Bag et al, 2021b); while the necessity for sustainable healthcare services is promoting significant attention towards the implementation of AI (Sood et al, 2022). When outlining research methodologies, the purpose of this section was to present a comprehensive and comparable list of methods.…”

Section: Descriptive Analysissupporting

confidence: 86%

“…The Covid-19 pandemic has highlighted the need for resilient and agile healthcare supply chains that respond quickly to unexpected disruption. The pandemic exposed vulnerabilities in global healthcare supply chains, including the urgent need for improving stockpiling and inventory management (Alrajhi et al, 2022), collaboration and information sharing (Cannavale et al, 2022), and sustainable and green healthcare supply chain (Sood et al, 2022). To enhance public health and the capacity for medical treatment, AI has been utilized in various areas of medical supply chain management, including demand forecasting, enabling the prediction of future demand for medical supplies and equipment; inventory management, facilitating real-time monitoring of inventory to reduce waste and inefficiencies by identifying stockouts and overstocking; quality control, allowing for monitoring of the quality of medical supplies and equipment to identify defects and anomalies; and route optimization, enabling the optimization of delivery routes for medical supplies and equipment based on factors such as policies, traffic, weather, and road conditions.…”

Section: Discussion With Practical Implicationsmentioning

confidence: 99%

“…The findings align with current research and industry trends, as the growing utilization of AI in the agricultural and food industry is propelled by the imperatives of food security (Bhatia and Albarrak, 2023), traceability (Hassoun et al , 2023), and sustainability (Sharma et al , 2022). Similarly, the adoption of AI in manufacturing supply chains is driven by the circular economy agenda (Bag et al , 2021b), while the pursuit of sustainable healthcare services has sparked substantial interest in AI implementation (Sood et al , 2022).…”

Section: Findings From Descriptive and Thematic Analysismentioning

confidence: 99%

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Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Hao

Demir

2023

JM2

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Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

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Section: Descriptive Analysissupporting

confidence: 86%

Section: Discussion With Practical Implicationsmentioning

confidence: 99%

Section: Findings From Descriptive and Thematic Analysismentioning

confidence: 99%

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Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Hao

Demir

2023

JM2

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“…Hood and Wilson [8] reviewed the evolution of various approaches to analyze the literature. Sood et al [9] offered a literature analysis of Industry 4.0 innovations from the perspective of COVID-19 by analyzing highly cited articles, contributing countries, prominent authors, journals, and keywords using CiteSpace. Coccia et al [10] represented the development status of quantum computing from 1990 to 2020.…”

Section: Literature Assessmentmentioning

confidence: 99%

Towards quantum state preparation with materials science: An analytical review

Sood

Chauhan

2023

Int J of Quantum Chemistry

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The field of quantum computing has undergone significant expansion and development in recent years. A quantitative and comprehensive examination of the research domain is essential to comprehend research trends and directions in the field. Henceforth, the current paper conducts a scientometric analysis of the academic literature on quantum computing retrieved from the Scopus database to understand the recent advances made by researchers in the domain. The analysis evaluates the state of the discipline's research by examining the most prominent journals and highly referenced articles. CiteSpace is leveraged to visualize the intellectual landscape of quantum state preparation by performing document co‐citation and author co‐citation analysis. The analysis reveals that trapped ions, photonic qubits, superconducting qubits, nitrogen‐vacancy centers, and nuclear spin qubits are the primary research topics in quantum computing. Furthermore, the most current research issues that require attention in the field are measurement‐based qubits, Majorana fermions, and quantum dots.

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“…Some scholars adopted qualitative methods to review some health technology research: [ 19 , 20 ]; others applied bibliometric methods to review some health technology systems and the application of emerging technologies in the health sector [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. However, these studies only focus on a specific piece of technology or application.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Health Technology Research: 1990~2020

Luo

et al. 2022

IJERPH

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This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016–2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.

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A visual review of artificial intelligence and Industry 4.0 in healthcare

Cited by 36 publications

References 25 publications

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Towards quantum state preparation with materials science: An analytical review

Bibliometric Analysis of Health Technology Research: 1990~2020

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